MLOps: From Models to Production
This course is designed to bridge the gap between academic ML concepts and the practical challenges of implementing these technologies in the real world. You will engage directly with the end-to-end lifecycle of a machine learning system, from the initial data handling to the final stages of deployment and monitoring.
Course taught by expert instructors
Yudhiesh Ravindranath
MLOps Engineer at MoneyLion
Yudhiesh is a seasoned Machine Learning Operations Engineer passionate about harnessing the power of machine learning to drive real business value swiftly and effortlessly. With a comprehensive understanding of the entire ML model development process, Yudhiesh excels in deploying and maintaining pipelines, centralizing experiment tracking, and leading teams to deliver impactful Machine Learning solutions.
The course
Learn and apply skills with real-world projects.
Software engineers - build production systems that integrate machine learning
Data scientists - learn about the production ML lifecycle (aka ‘what comes after model training’)
Students/recent college grads - hands-on experience with building and shipping production-ready ML applications
Familiarity with fundamental machine learning concepts (ML problems such as classification and regression, model training and testing, loss functions, backpropagation etc).
Familiarity with software development in Python
Familiarity with Docker
Nice to have: Familiarity with CI/CD tools such as Github Actions
Try these prep courses first
- Learn
- Introduction to ML Lifecycle and Tools
- Overview of MLOps
- Data Science Development Environment
- Experiment Tracking
- Model Registry
- Testing in Machine Learning: Pre-train tests, Train-tests, Post-train tests
Project: Train a model to classify the sentiment of user reviews- EDA to understand the data
- Create pre-train tests such as dataset expectations
- Train a model looking at comparing simple -> complex models in terms of model metrics, performance metrics, etc. while tracking experiment results and models
- Run post-train tests to ensure expected learned behavior
- Learn
- Model deployments methods: Batch/Streaming/Real time
- Feature Stores: What, Why, and How
- Model performance optimization: Concurrency, Quantization/Model Distillation, and Caching
- Ray Serve
- API Endpoint Monitoring & Observability
Project: Deploy our ML Model to a Ray Serve API Endpoint- Deploy basic a ML model
- Setup Monitoring & Observability for API Endpoint
- Load test endpoint
- Optimize ML model and load test accordingly
- Test API Endpoint using Pytest
- Learn
- Monitoring ML Models: User feedback, performance metrics, data quality, drift, and system metrics
- Types of Drift
- Continual Learning
Project: Monitoring & Debugging the ML Model- Simulate data drift and try to debug why it happened
- Using an LLM to automatically label data
- Offline Evaluation comparing the champion and contender model
- Deploy the model using a canary rollout
A course you'll actually complete. AI-powered learning that drives results.
AI-powered learning
Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.
Live courses by leading experts
Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.
Practice on the cutting edge
Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.
Flexible schedule for busy professionals
We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.
Completion certificates
Each course comes with a certificate for learners to add to their resume.
Best-in-class outcomes
15-20x engagement compared to async courses
Support & accountability
You are never alone, we provide support throughout the course.
Get reimbursed by your company
More than half of learners get their Courses and Memberships reimbursed by their company.
Hundreds of companies have dedicated L&D and education budgets that have covered the costs.
Course success stories
Learn together and share experiences with other industry professionals
Nihit has a rare set of skills and experiences - building large-scale ML production systems at top companies, along with a solid and rigorous research background. Along with that, he is great at distilling and passing on his hard-won insights and knowledge. I've learned a lot from his newsletter and the talks he's given to large audiences at Upstart - so I know first-hand how valuable and practical this class will be, and can't think of a better instructor!
Nihit has extensive experience building ML systems for recommendations, ranking and integrity problems at Facebook and LinkedIn. His expertise lies not only in developing and improving deep learning techniques but also in working with large scale systems that scale to billions of users. It’s a combination of both these skill sets that makes him a great fit to teach an MLOps course that requires an in-depth understanding of ML fundamentals and the ability to build out scalable systems that deal with constantly growing and ever-changing datasets in the real-world.
Nihit combines a deep theoretical understanding of ML with hands-on practical knowledge from having built large-scale search, recommender, and decisioning ML systems at the most impactful Internet companies. If I had to learn how to go from an idea to a working, scalable ML system, there would be no better instructor than Nihit!
Everything about this course is awesome and exactly what I was looking for. I love how they put into consideration the different levels of experience of participants and set up helpful coding parties. The lectures & content are very detailed. I also learned a lot while trying out the projects, and the community is simply the best. Big thanks to the course manager for checking in and boosting my morale, the TA for leading the weekly coding parties (those were super helpful) and of course, Nihit. Thank you Uplimit!
Amazing course! Addresses well the challenges that exist in the development of a real machine learning pipeline and demonstrates techniques and tools on how to solve it. The community is really helpful!
